Mapeo de COVID-19¶

Base de datos¶

In [2]:
import pandas as pd
df = pd.read_csv("C:/Users/camil/Desktop/Cato/ciclo 10/Herramientas cuantitativas/maps/positivos_covid.csv", delimiter=';')
In [3]:
print(df.head())

   FECHA_CORTE DEPARTAMENTO  PROVINCIA     DISTRITO METODODX  EDAD       SEXO  \
0     20241203       TUMBES     TUMBES       TUMBES       AG  46.0   FEMENINO   
1     20241203         LIMA       LIMA  JESUS MARIA       AG  69.0   FEMENINO   
2     20241203   SAN MARTIN  MOYOBAMBA    MOYOBAMBA       AG  55.0   FEMENINO   
3     20241203     AREQUIPA   CAYLLOMA    COPORAQUE       AG  50.0  MASCULINO   
4     20241203         LIMA       LIMA  JESUS MARIA       AG  58.0  MASCULINO   

   FECHA_RESULTADO    UBIGEO  id_persona  
0       20221207.0  240101.0    203499.0  
1       20230822.0  150113.0    221397.0  
2       20240108.0  220101.0    295651.0  
3       20230824.0   40506.0    851625.0  
4       20221217.0  150113.0    287786.0
In [4]:
df=df[df.EDAD>50]
In [5]:
df.METODODX.value_counts()
Out[5]:
METODODX
AG     599842
PCR    400134
PR     293382
Name: count, dtype: int64
In [6]:
# NUEVA VARIABLE "AÑO"

df['FECHA_RESULTADO'] = pd.to_datetime(df['FECHA_RESULTADO'], format='%Y%m%d', errors='coerce')
In [7]:
df['AÑO'] = df['FECHA_RESULTADO'].dt.year
In [8]:
df.AÑO.head()
Out[8]:
1    2023.0
2    2024.0
4    2022.0
7    2023.0
9    2020.0
Name: AÑO, dtype: float64
In [9]:
df['AÑO'] = df['AÑO'].astype('Int64') #a numeros enteros
In [10]:
print(df[['AÑO']].head())

    AÑO
1  2023
2  2024
4  2022
7  2023
9  2020

Agreggate¶

In [11]:
# AG: Antigenos
# PCR: Prueba molecular
# PR: Prueba rápida serológica
In [12]:
df = df[df['AÑO'] != 1899]
In [13]:
df = df[df['PROVINCIA'] != 'EN INVESTIGACIÓN']
In [14]:
indexList=['AÑO','DEPARTAMENTO','PROVINCIA','METODODX']
aggregator={'METODODX':[len]}
prov=df.groupby(indexList,observed=True).agg(aggregator)
prov
Out[14]:
METODODX
len
AÑO DEPARTAMENTO PROVINCIA METODODX
2020 AMAZONAS BAGUA PCR 203
PR 2368
BONGARA PCR 20
PR 92
CHACHAPOYAS PCR 90
... ... ... ... ...
2024 TUMBES TUMBES PCR 15
ZARUMILLA PCR 6
UCAYALI CORONEL PORTILLO AG 16
PCR 17
PADRE ABAD AG 1

1886 rows × 1 columns

In [15]:
#wide
Draft=prov.unstack(3).fillna(0) #leftmost index in rows
Draft
Out[15]:
METODODX
len
METODODX AG PCR PR
AÑO DEPARTAMENTO PROVINCIA
2020 AMAZONAS BAGUA 0.0 203.0 2368.0
BONGARA 0.0 20.0 92.0
CHACHAPOYAS 0.0 90.0 419.0
CONDORCANQUI 0.0 14.0 540.0
LUYA 0.0 1.0 89.0
... ... ... ... ... ...
2024 TUMBES CONTRALMIRANTE VILLAR 0.0 4.0 0.0
TUMBES 11.0 15.0 0.0
ZARUMILLA 0.0 6.0 0.0
UCAYALI CORONEL PORTILLO 16.0 17.0 0.0
PADRE ABAD 1.0 0.0 0.0

920 rows × 3 columns

In [16]:
# % de personas con prueba AG
Draft['AG_pct']=Draft.iloc[:,1]/(Draft.iloc[:,0] + Draft.iloc[:,1])
prov_Ag=Draft['AG_pct'].unstack('AÑO').fillna(0)
prov_Ag
Out[16]:
AÑO 2020 2021 2022 2023 2024
DEPARTAMENTO PROVINCIA
AMAZONAS BAGUA 1.000000 0.144654 0.233813 0.240000 0.470588
BONGARA 1.000000 0.291971 0.466019 0.454545 0.928571
CHACHAPOYAS 1.000000 0.448919 0.414784 0.219178 0.322581
CONDORCANQUI 1.000000 0.033333 0.025000 0.000000 0.000000
LUYA 1.000000 0.036585 0.059361 0.111111 0.750000
... ... ... ... ... ... ...
TUMBES ZARUMILLA 0.923077 0.315556 0.492958 0.588235 1.000000
UCAYALI ATALAYA 0.000000 0.000000 0.000000 0.000000 0.000000
CORONEL PORTILLO 0.996875 0.186831 0.503120 0.545455 0.515152
PADRE ABAD 0.000000 0.078512 0.193694 0.000000 0.000000
PURUS 0.000000 0.000000 0.000000 1.000000 0.000000

196 rows × 5 columns

In [17]:
#data type
prov_Ag.columns #esta como numero los años
Out[17]:
Index([2020, 2021, 2022, 2023, 2024], dtype='Int64', name='AÑO')
In [18]:
prov_Ag.columns=['year'+str(x) for x in prov_Ag.columns]
In [19]:
prov_Ag
Out[19]:
year2020 year2021 year2022 year2023 year2024
DEPARTAMENTO PROVINCIA
AMAZONAS BAGUA 1.000000 0.144654 0.233813 0.240000 0.470588
BONGARA 1.000000 0.291971 0.466019 0.454545 0.928571
CHACHAPOYAS 1.000000 0.448919 0.414784 0.219178 0.322581
CONDORCANQUI 1.000000 0.033333 0.025000 0.000000 0.000000
LUYA 1.000000 0.036585 0.059361 0.111111 0.750000
... ... ... ... ... ... ...
TUMBES ZARUMILLA 0.923077 0.315556 0.492958 0.588235 1.000000
UCAYALI ATALAYA 0.000000 0.000000 0.000000 0.000000 0.000000
CORONEL PORTILLO 0.996875 0.186831 0.503120 0.545455 0.515152
PADRE ABAD 0.000000 0.078512 0.193694 0.000000 0.000000
PURUS 0.000000 0.000000 0.000000 1.000000 0.000000

196 rows × 5 columns

In [20]:
prov_Ag.reset_index(inplace=True)
prov_Ag
Out[20]:
DEPARTAMENTO PROVINCIA year2020 year2021 year2022 year2023 year2024
0 AMAZONAS BAGUA 1.000000 0.144654 0.233813 0.240000 0.470588
1 AMAZONAS BONGARA 1.000000 0.291971 0.466019 0.454545 0.928571
2 AMAZONAS CHACHAPOYAS 1.000000 0.448919 0.414784 0.219178 0.322581
3 AMAZONAS CONDORCANQUI 1.000000 0.033333 0.025000 0.000000 0.000000
4 AMAZONAS LUYA 1.000000 0.036585 0.059361 0.111111 0.750000
... ... ... ... ... ... ... ...
191 TUMBES ZARUMILLA 0.923077 0.315556 0.492958 0.588235 1.000000
192 UCAYALI ATALAYA 0.000000 0.000000 0.000000 0.000000 0.000000
193 UCAYALI CORONEL PORTILLO 0.996875 0.186831 0.503120 0.545455 0.515152
194 UCAYALI PADRE ABAD 0.000000 0.078512 0.193694 0.000000 0.000000
195 UCAYALI PURUS 0.000000 0.000000 0.000000 1.000000 0.000000

196 rows × 7 columns

MAPA¶

In [21]:
mapLink='https://github.com/SocialAnalytics-StrategicIntelligence/GeoDF_Analytics/raw/main/maps/ProvsINEI2023.zip'
#panadas vienen como zip hy, r no puede abrir por si acaso

import geopandas as gpd

provmap=gpd.read_file(mapLink)

provmap.info()

<class 'geopandas.geodataframe.GeoDataFrame'>
RangeIndex: 196 entries, 0 to 195
Data columns (total 6 columns):
 #   Column      Non-Null Count  Dtype   
---  ------      --------------  -----   
 0   OBJECTID    196 non-null    float64 
 1   CCDD        196 non-null    object  
 2   CCPP        196 non-null    object  
 3   DEPARTAMEN  196 non-null    object  
 4   PROVINCIA   196 non-null    object  
 5   geometry    196 non-null    geometry
dtypes: float64(1), geometry(1), object(4)
memory usage: 9.3+ KB
In [22]:
provmap['location']=['+'.join(x[0]) for x in zip(provmap.iloc[:,3:5].values)]
provmap.head(10)
Out[22]:
OBJECTID CCDD CCPP DEPARTAMEN PROVINCIA geometry location
0 1.0 01 01 AMAZONAS CHACHAPOYAS POLYGON ((-77.72614 -5.94354, -77.72486 -5.943... AMAZONAS+CHACHAPOYAS
1 2.0 01 02 AMAZONAS BAGUA POLYGON ((-78.61909 -4.51001, -78.61802 -4.510... AMAZONAS+BAGUA
2 3.0 01 03 AMAZONAS BONGARA POLYGON ((-77.72759 -5.1403, -77.72361 -5.1406... AMAZONAS+BONGARA
3 4.0 01 04 AMAZONAS CONDORCANQUI POLYGON ((-77.81399 -2.99278, -77.81483 -2.995... AMAZONAS+CONDORCANQUI
4 5.0 01 05 AMAZONAS LUYA POLYGON ((-78.13023 -5.9037, -78.13011 -5.9041... AMAZONAS+LUYA
5 6.0 01 06 AMAZONAS RODRIGUEZ DE MENDOZA POLYGON ((-77.44452 -6.05002, -77.44387 -6.050... AMAZONAS+RODRIGUEZ DE MENDOZA
6 7.0 01 07 AMAZONAS UTCUBAMBA POLYGON ((-78.09288 -5.36258, -78.09288 -5.364... AMAZONAS+UTCUBAMBA
7 8.0 02 01 ANCASH HUARAZ POLYGON ((-77.3987 -9.35563, -77.39852 -9.3560... ANCASH+HUARAZ
8 9.0 02 02 ANCASH AIJA POLYGON ((-77.61368 -9.649, -77.61241 -9.64975... ANCASH+AIJA
9 10.0 02 03 ANCASH ANTONIO RAYMONDI POLYGON ((-77.08856 -8.97496, -77.08804 -8.975... ANCASH+ANTONIO RAYMONDI
In [23]:
print(prov_Ag.columns)

Index(['DEPARTAMENTO', 'PROVINCIA', 'year2020', 'year2021', 'year2022',
       'year2023', 'year2024'],
      dtype='object')
In [24]:
# Unir las columnas 'departamento' y 'provincia' en una nueva columna 'location'
prov_Ag['location'] = prov_Ag.apply(lambda row: f"{row['DEPARTAMENTO']} + {row['PROVINCIA']}", axis=1)

# Ver las primeras filas para confirmar
prov_Ag.head()
Out[24]:
DEPARTAMENTO PROVINCIA year2020 year2021 year2022 year2023 year2024 location
0 AMAZONAS BAGUA 1.0 0.144654 0.233813 0.240000 0.470588 AMAZONAS + BAGUA
1 AMAZONAS BONGARA 1.0 0.291971 0.466019 0.454545 0.928571 AMAZONAS + BONGARA
2 AMAZONAS CHACHAPOYAS 1.0 0.448919 0.414784 0.219178 0.322581 AMAZONAS + CHACHAPOYAS
3 AMAZONAS CONDORCANQUI 1.0 0.033333 0.025000 0.000000 0.000000 AMAZONAS + CONDORCANQUI
4 AMAZONAS LUYA 1.0 0.036585 0.059361 0.111111 0.750000 AMAZONAS + LUYA
In [25]:
## Limpieza
In [26]:
!pip install unidecode

Requirement already satisfied: unidecode in c:\users\camil\anaconda3\envs\herramientas\lib\site-packages (1.3.8)
In [27]:
import unidecode

byePunctuation=lambda x: unidecode.unidecode(x)
prov_Ag['location']=prov_Ag['location'].apply(byePunctuation)
provmap['location']=provmap['location'].apply(byePunctuation)
In [28]:
prov_Ag['location']=prov_Ag.location.str.replace("\-|\_|\s+","",regex=True)
provmap['location']=provmap.location.str.replace("\-|\_|\s+","",regex=True)

<>:1: SyntaxWarning: invalid escape sequence '\-'
<>:2: SyntaxWarning: invalid escape sequence '\-'
<>:1: SyntaxWarning: invalid escape sequence '\-'
<>:2: SyntaxWarning: invalid escape sequence '\-'
C:\Users\camil\AppData\Local\Temp\ipykernel_30244\3744470951.py:1: SyntaxWarning: invalid escape sequence '\-'
  prov_Ag['location']=prov_Ag.location.str.replace("\-|\_|\s+","",regex=True)
C:\Users\camil\AppData\Local\Temp\ipykernel_30244\3744470951.py:2: SyntaxWarning: invalid escape sequence '\-'
  provmap['location']=provmap.location.str.replace("\-|\_|\s+","",regex=True)
In [29]:
nomatch_df=set(prov_Ag.location)- set(provmap.location)
nomatch_gdf=set(provmap.location)-set(prov_Ag.location)
In [30]:
len(nomatch_df), len(nomatch_gdf) #!!!
Out[30]:
(2, 2)
In [31]:
!pip install thefuzz

Requirement already satisfied: thefuzz in c:\users\camil\anaconda3\envs\herramientas\lib\site-packages (0.22.1)
Requirement already satisfied: rapidfuzz<4.0.0,>=3.0.0 in c:\users\camil\anaconda3\envs\herramientas\lib\site-packages (from thefuzz) (3.9.7)
In [32]:
# pick the closest match from nomatch_gdf for a value in nomatch_df
from thefuzz import process
[(dis,process.extractOne(dis,nomatch_gdf)) for dis in sorted(nomatch_df)]
Out[32]:
[('ANCASH+ANTONIORAIMONDI', ('ANCASH+ANTONIORAYMONDI', 95)),
 ('ICA+NAZCA', ('ICA+NASCA', 89))]
In [33]:
# Diccionario de cambios
{dis:process.extractOne(dis,nomatch_gdf)[0] for dis in sorted(nomatch_df)}
Out[33]:
{'ANCASH+ANTONIORAIMONDI': 'ANCASH+ANTONIORAYMONDI', 'ICA+NAZCA': 'ICA+NASCA'}
In [34]:
# then:
changesinDF={dis:process.extractOne(dis,nomatch_gdf)[0] for dis in sorted(nomatch_df)}
In [35]:
prov_Ag.replace({'location': changesinDF}, inplace=True)
In [36]:
# comprobamos
In [37]:
nomatch_df=set(prov_Ag.location)- set(provmap.location)
nomatch_gdf=set(provmap.location)-set(prov_Ag.location)

[(dis,process.extractOne(dis,nomatch_gdf)) for dis in sorted(nomatch_df)]
Out[37]:
[]
In [38]:
#merge
prov_Ag_map=provmap.merge(prov_Ag, on='location',how='left',indicator='flag')
In [39]:
prov_Ag_map.info()

<class 'geopandas.geodataframe.GeoDataFrame'>
RangeIndex: 196 entries, 0 to 195
Data columns (total 15 columns):
 #   Column        Non-Null Count  Dtype   
---  ------        --------------  -----   
 0   OBJECTID      196 non-null    float64 
 1   CCDD          196 non-null    object  
 2   CCPP          196 non-null    object  
 3   DEPARTAMEN    196 non-null    object  
 4   PROVINCIA_x   196 non-null    object  
 5   geometry      196 non-null    geometry
 6   location      196 non-null    object  
 7   DEPARTAMENTO  196 non-null    object  
 8   PROVINCIA_y   196 non-null    object  
 9   year2020      196 non-null    float64 
 10  year2021      196 non-null    float64 
 11  year2022      196 non-null    float64 
 12  year2023      196 non-null    float64 
 13  year2024      196 non-null    float64 
 14  flag          196 non-null    category
dtypes: category(1), float64(6), geometry(1), object(7)
memory usage: 21.9+ KB
In [40]:
prov_Ag_map['flag']=prov_Ag_map.flag.astype(str)
In [41]:
#
bye=['DEPARTAMEN', 'PROVINCIA_x', 'CCPP','CCDD']
prov_Ag_map.drop(columns=bye,inplace=True)
prov_Ag_map.head()
Out[41]:
OBJECTID geometry location DEPARTAMENTO PROVINCIA_y year2020 year2021 year2022 year2023 year2024 flag
0 1.0 POLYGON ((-77.72614 -5.94354, -77.72486 -5.943... AMAZONAS+CHACHAPOYAS AMAZONAS CHACHAPOYAS 1.0 0.448919 0.414784 0.219178 0.322581 both
1 2.0 POLYGON ((-78.61909 -4.51001, -78.61802 -4.510... AMAZONAS+BAGUA AMAZONAS BAGUA 1.0 0.144654 0.233813 0.240000 0.470588 both
2 3.0 POLYGON ((-77.72759 -5.1403, -77.72361 -5.1406... AMAZONAS+BONGARA AMAZONAS BONGARA 1.0 0.291971 0.466019 0.454545 0.928571 both
3 4.0 POLYGON ((-77.81399 -2.99278, -77.81483 -2.995... AMAZONAS+CONDORCANQUI AMAZONAS CONDORCANQUI 1.0 0.033333 0.025000 0.000000 0.000000 both
4 5.0 POLYGON ((-78.13023 -5.9037, -78.13011 -5.9041... AMAZONAS+LUYA AMAZONAS LUYA 1.0 0.036585 0.059361 0.111111 0.750000 both
In [42]:
# lidiar con los CEROS
prov_Ag_map.fillna(0,inplace=True)

Guardar el geoDF

In [43]:
#crear carpeta maps, donde guardar

import os
prov_Ag_map.to_file(os.path.join('C:/Users/camil/Desktop/Cato/ciclo 10/Herramientas cuantitativas/maps',"provinciasPeru.gpkg"), layer='provinciasDengue', driver="GPKG")

Explorar variables¶

In [44]:
# statistics
prov_Ag_map.year2022.describe()
Out[44]:
count    196.000000
mean       0.125021
std        0.161102
min        0.000000
25%        0.016327
50%        0.068471
75%        0.170919
max        1.000000
Name: year2022, dtype: float64
In [45]:
! pip install seaborn

Requirement already satisfied: seaborn in c:\users\camil\anaconda3\envs\herramientas\lib\site-packages (0.13.2)
Requirement already satisfied: numpy!=1.24.0,>=1.20 in c:\users\camil\anaconda3\envs\herramientas\lib\site-packages (from seaborn) (2.0.2)
Requirement already satisfied: pandas>=1.2 in c:\users\camil\anaconda3\envs\herramientas\lib\site-packages (from seaborn) (2.2.2)
Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in c:\users\camil\anaconda3\envs\herramientas\lib\site-packages (from seaborn) (3.9.2)
Requirement already satisfied: contourpy>=1.0.1 in c:\users\camil\anaconda3\envs\herramientas\lib\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.3.0)
Requirement already satisfied: cycler>=0.10 in c:\users\camil\anaconda3\envs\herramientas\lib\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.12.1)
Requirement already satisfied: fonttools>=4.22.0 in c:\users\camil\anaconda3\envs\herramientas\lib\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.53.1)
Requirement already satisfied: kiwisolver>=1.3.1 in c:\users\camil\anaconda3\envs\herramientas\lib\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.7)
Requirement already satisfied: packaging>=20.0 in c:\users\camil\anaconda3\envs\herramientas\lib\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (24.1)
Requirement already satisfied: pillow>=8 in c:\users\camil\anaconda3\envs\herramientas\lib\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (10.4.0)
Requirement already satisfied: pyparsing>=2.3.1 in c:\users\camil\anaconda3\envs\herramientas\lib\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.1.4)
Requirement already satisfied: python-dateutil>=2.7 in c:\users\camil\anaconda3\envs\herramientas\lib\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.9.0.post0)
Requirement already satisfied: pytz>=2020.1 in c:\users\camil\anaconda3\envs\herramientas\lib\site-packages (from pandas>=1.2->seaborn) (2024.1)
Requirement already satisfied: tzdata>=2022.7 in c:\users\camil\anaconda3\envs\herramientas\lib\site-packages (from pandas>=1.2->seaborn) (2024.1)
Requirement already satisfied: six>=1.5 in c:\users\camil\anaconda3\envs\herramientas\lib\site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0)
In [46]:
# grafico 
import seaborn as sea

sea.boxplot(prov_Ag_map.year2022, color='yellow',orient='h')
Out[46]:
<Axes: xlabel='year2022'>
No description has been provided for this image

Mas del 70% de las provincias no llegan al 40% de realización de pruebas antigenas para detectar covid 19.

In [47]:
#grafica de trasnformacion cuantilica?
from sklearn.preprocessing import QuantileTransformer
qt = QuantileTransformer(n_quantiles=100, random_state=0,output_distribution='normal')
qt_result=qt.fit_transform(prov_Ag_map[['year2022']])
sea.boxplot(qt_result, color='yellow',orient='h')
Out[47]:
<Axes: >
No description has been provided for this image
In [48]:
prov_Ag_map['year_2022_qt']= qt_result

Correlación Espacial¶

Vecindario

In [49]:
!pip install pysal

Requirement already satisfied: pysal in c:\users\camil\anaconda3\envs\herramientas\lib\site-packages (24.7)
Requirement already satisfied: beautifulsoup4>=4.10 in c:\users\camil\anaconda3\envs\herramientas\lib\site-packages (from pysal) (4.12.3)
Requirement already satisfied: geopandas>=0.10.0 in c:\users\camil\anaconda3\envs\herramientas\lib\site-packages (from pysal) (1.0.1)
Requirement already satisfied: numpy>=1.22 in c:\users\camil\anaconda3\envs\herramientas\lib\site-packages (from pysal) (2.0.2)
Requirement already satisfied: packaging>=22 in c:\users\camil\anaconda3\envs\herramientas\lib\site-packages (from pysal) (24.1)
Requirement already satisfied: pandas>=1.4 in c:\users\camil\anaconda3\envs\herramientas\lib\site-packages (from pysal) (2.2.2)
Requirement already satisfied: platformdirs>=2.0.2 in c:\users\camil\anaconda3\envs\herramientas\lib\site-packages (from pysal) (3.10.0)
Requirement already satisfied: requests>=2.27 in c:\users\camil\anaconda3\envs\herramientas\lib\site-packages (from pysal) (2.32.3)
Requirement already satisfied: scipy>=1.8 in c:\users\camil\anaconda3\envs\herramientas\lib\site-packages (from pysal) (1.14.1)
Requirement already satisfied: shapely>=2.0.1 in c:\users\camil\anaconda3\envs\herramientas\lib\site-packages (from pysal) (2.0.6)
Requirement already satisfied: scikit-learn>=1.1 in c:\users\camil\anaconda3\envs\herramientas\lib\site-packages (from pysal) (1.5.1)
Requirement already satisfied: libpysal>=4.12.0 in c:\users\camil\anaconda3\envs\herramientas\lib\site-packages (from pysal) (4.12.1)
Requirement already satisfied: access>=1.1.9 in c:\users\camil\anaconda3\envs\herramientas\lib\site-packages (from pysal) (1.1.9)
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Requirement already satisfied: giddy>=2.3.5 in c:\users\camil\anaconda3\envs\herramientas\lib\site-packages (from pysal) (2.3.5)
Requirement already satisfied: inequality>=1.0.1 in c:\users\camil\anaconda3\envs\herramientas\lib\site-packages (from pysal) (1.0.1)
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In [50]:
import numpy as np
print(np.__version__)

2.0.2
In [51]:
pip install numpy==1.23.4

Collecting numpy==1.23.4
  Using cached numpy-1.23.4.tar.gz (10.7 MB)
  Installing build dependencies: started
  Installing build dependencies: finished with status 'done'
  Getting requirements to build wheel: started
  Getting requirements to build wheel: finished with status 'error'
Note: you may need to restart the kernel to use updated packages.

  error: subprocess-exited-with-error
  
  Getting requirements to build wheel did not run successfully.
  exit code: 1
  
  [33 lines of output]
  Traceback (most recent call last):
    File "C:\Users\camil\anaconda3\envs\herramientas\Lib\site-packages\pip\_vendor\pyproject_hooks\_in_process\_in_process.py", line 353, in <module>
      main()
    File "C:\Users\camil\anaconda3\envs\herramientas\Lib\site-packages\pip\_vendor\pyproject_hooks\_in_process\_in_process.py", line 335, in main
      json_out['return_val'] = hook(**hook_input['kwargs'])
                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    File "C:\Users\camil\anaconda3\envs\herramientas\Lib\site-packages\pip\_vendor\pyproject_hooks\_in_process\_in_process.py", line 112, in get_requires_for_build_wheel
      backend = _build_backend()
                ^^^^^^^^^^^^^^^^
    File "C:\Users\camil\anaconda3\envs\herramientas\Lib\site-packages\pip\_vendor\pyproject_hooks\_in_process\_in_process.py", line 77, in _build_backend
      obj = import_module(mod_path)
            ^^^^^^^^^^^^^^^^^^^^^^^
    File "C:\Users\camil\anaconda3\envs\herramientas\Lib\importlib\__init__.py", line 90, in import_module
      return _bootstrap._gcd_import(name[level:], package, level)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    File "<frozen importlib._bootstrap>", line 1387, in _gcd_import
    File "<frozen importlib._bootstrap>", line 1360, in _find_and_load
    File "<frozen importlib._bootstrap>", line 1310, in _find_and_load_unlocked
    File "<frozen importlib._bootstrap>", line 488, in _call_with_frames_removed
    File "<frozen importlib._bootstrap>", line 1387, in _gcd_import
    File "<frozen importlib._bootstrap>", line 1360, in _find_and_load
    File "<frozen importlib._bootstrap>", line 1331, in _find_and_load_unlocked
    File "<frozen importlib._bootstrap>", line 935, in _load_unlocked
    File "<frozen importlib._bootstrap_external>", line 995, in exec_module
    File "<frozen importlib._bootstrap>", line 488, in _call_with_frames_removed
    File "C:\Users\camil\AppData\Local\Temp\pip-build-env-umy30tp6\overlay\Lib\site-packages\setuptools\__init__.py", line 16, in <module>
      import setuptools.version
    File "C:\Users\camil\AppData\Local\Temp\pip-build-env-umy30tp6\overlay\Lib\site-packages\setuptools\version.py", line 1, in <module>
      import pkg_resources
    File "C:\Users\camil\AppData\Local\Temp\pip-build-env-umy30tp6\overlay\Lib\site-packages\pkg_resources\__init__.py", line 2172, in <module>
      register_finder(pkgutil.ImpImporter, find_on_path)
                      ^^^^^^^^^^^^^^^^^^^
  AttributeError: module 'pkgutil' has no attribute 'ImpImporter'. Did you mean: 'zipimporter'?
  [end of output]
  
  note: This error originates from a subprocess, and is likely not a problem with pip.
error: subprocess-exited-with-error

Getting requirements to build wheel did not run successfully.
exit code: 1

See above for output.

note: This error originates from a subprocess, and is likely not a problem with pip.
In [52]:
from libpysal.weights import Queen, Rook, KNN
In [53]:
# rook
w_rook = Rook.from_dataframe(prov_Ag_map,use_index=False)
In [54]:
# queen
w_queen = Queen.from_dataframe(prov_Ag_map,use_index=False)
In [55]:
# k nearest neighbors
w_knn = KNN.from_dataframe(prov_Ag_map, k=8) #quiero 8 mas cercanos a mi.

Moran's correlation¶

In [56]:
# needed for spatial correlation
w_queen.transform = 'R'
In [57]:
pd.DataFrame(*w_queen.full()).sum(axis=1) # 1 means both are neighbors
Out[57]:
0      1.0
1      1.0
2      1.0
3      1.0
4      1.0
      ... 
191    1.0
192    1.0
193    1.0
194    1.0
195    1.0
Length: 196, dtype: float64

INDICE DE MORAN

Quiero ver si hay correlacion entre mi vecindario cocovid.ias.

In [58]:
from esda.moran import Moran

moranCOVID = Moran(prov_Ag_map['year_2022_qt'], w_queen)
moranCOVID.I,moranCOVID.p_sim

#correlacion a 0. y es significativa por es 0.001 ***
Out[58]:
(np.float64(0.19136370498258676), np.float64(0.001))

interpretacion:Correlación de baja (0.19), pero sí sale significativo. Entonces, el virus sí tiene una variable espacial.

Grafico

In [59]:
from splot.esda import moran_scatterplot
import matplotlib.pyplot as plt

fig, ax = moran_scatterplot(moranCOVID)
ax.set_xlabel('Covid_prueba_share')
ax.set_ylabel('SpatialLag_Covid_prueba_share')
Out[59]:
Text(0, 0.5, 'SpatialLag_Covid_prueba_share')
No description has been provided for this image

Otro grafico

In [60]:
# The scatterplot with local info

from esda.moran import Moran_Local

# calculate Moran_Local and plot
lisaCOVID = Moran_Local(y=prov_Ag_map['year_2022_qt'], w=w_knn,seed=2022)
fig, ax = moran_scatterplot(lisaCOVID,p=0.05)
ax.set_xlabel('Covid_prueba_share')
ax.set_ylabel('SpatialLag_Covid_prueba_share');
No description has been provided for this image

La mayoría de casos se encuentran en el cuadrante a la derecha. .

Mapa de peru

In [61]:
# the map with the spots and outliers

from splot.esda import lisa_cluster
f, ax = plt.subplots(1, figsize=(12, 12))
plt.title('Spots and Outliers')
fig = lisa_cluster(lisaCOVID,
                   prov_Ag_map,ax=ax,
                   legend_kwds={'loc': 'center left',
                                'bbox_to_anchor': (0.7, 0.6)})
No description has been provided for this image
In [62]:
lisaCOVID.q
Out[62]:
array([1, 1, 1, 1, 1, 1, 1, 4, 3, 3, 3, 3, 3, 3, 4, 3, 3, 4, 4, 3, 3, 3,
       3, 3, 4, 3, 3, 1, 4, 1, 4, 1, 4, 1, 4, 3, 3, 3, 4, 3, 4, 3, 1, 3,
       3, 1, 1, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 3, 2, 2, 1, 2, 2, 1, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 4, 3, 3, 4, 3, 1, 1, 4, 3, 1, 1, 4, 4, 1, 1, 1, 1, 1, 2, 1, 1,
       2, 1, 1, 1, 1, 2, 4, 1, 1, 3, 4, 4, 1, 4, 1, 1, 1, 1, 4, 3, 1, 1,
       1, 1, 4, 3, 1, 1, 1, 1, 4, 1, 2, 1, 2, 4, 2, 4, 4, 2, 4, 1, 4, 1,
       4, 1, 2, 4, 1, 1, 1, 2, 4, 3, 2, 4, 4, 4, 4, 1, 2, 3, 4, 4, 4, 1,
       4, 2, 1, 1, 1, 1, 1, 1, 4, 4, 3, 3, 3, 1, 1, 1, 4, 2, 1, 3])
In [63]:
# significance
lisaCOVID.p_sim
Out[63]:
array([0.165, 0.301, 0.099, 0.016, 0.051, 0.129, 0.029, 0.209, 0.087,
       0.002, 0.012, 0.012, 0.013, 0.004, 0.335, 0.11 , 0.005, 0.09 ,
       0.017, 0.013, 0.079, 0.13 , 0.002, 0.017, 0.132, 0.003, 0.02 ,
       0.152, 0.181, 0.441, 0.058, 0.114, 0.239, 0.075, 0.404, 0.084,
       0.019, 0.098, 0.181, 0.066, 0.157, 0.132, 0.461, 0.333, 0.179,
       0.023, 0.332, 0.009, 0.02 , 0.008, 0.031, 0.052, 0.164, 0.135,
       0.028, 0.052, 0.005, 0.121, 0.005, 0.101, 0.083, 0.142, 0.066,
       0.049, 0.047, 0.025, 0.04 , 0.298, 0.22 , 0.225, 0.451, 0.232,
       0.301, 0.269, 0.26 , 0.265, 0.243, 0.38 , 0.305, 0.195, 0.021,
       0.039, 0.029, 0.095, 0.014, 0.469, 0.015, 0.406, 0.5  , 0.059,
       0.001, 0.013, 0.077, 0.006, 0.498, 0.015, 0.073, 0.408, 0.499,
       0.016, 0.37 , 0.331, 0.015, 0.036, 0.173, 0.214, 0.172, 0.217,
       0.028, 0.271, 0.223, 0.095, 0.085, 0.184, 0.035, 0.014, 0.373,
       0.036, 0.145, 0.028, 0.231, 0.2  , 0.066, 0.309, 0.03 , 0.067,
       0.333, 0.036, 0.017, 0.091, 0.242, 0.103, 0.475, 0.016, 0.159,
       0.034, 0.017, 0.307, 0.144, 0.056, 0.369, 0.264, 0.093, 0.046,
       0.031, 0.358, 0.11 , 0.141, 0.039, 0.357, 0.058, 0.264, 0.056,
       0.112, 0.31 , 0.15 , 0.001, 0.234, 0.159, 0.003, 0.006, 0.122,
       0.03 , 0.332, 0.456, 0.012, 0.081, 0.41 , 0.369, 0.481, 0.275,
       0.327, 0.152, 0.392, 0.044, 0.083, 0.034, 0.136, 0.137, 0.21 ,
       0.465, 0.42 , 0.078, 0.438, 0.001, 0.036, 0.218, 0.069, 0.149,
       0.009, 0.011, 0.011, 0.327, 0.488, 0.333, 0.207])
In [64]:
# quadrant: 1 HH,  2 LH,  3 LL,  4 HL
pd.Series(lisaCOVID.q).value_counts()
Out[64]:
1    94
4    43
3    42
2    17
Name: count, dtype: int64
In [65]:
prov_Ag_map['COVID_quadrant']=[l if p <0.05 else 0 for l,p in zip(lisaCOVID.q,lisaCOVID.p_sim)  ]
prov_Ag_map['COVID_quadrant'].value_counts()

#Agregar a la correlación espacial
Out[65]:
COVID_quadrant
0    131
1     32
3     21
4      9
2      3
Name: count, dtype: int64
In [66]:
#recodificar
labels = [ '0 no_sig', '1 hotSpot', '2 coldOutlier', '3 coldSpot', '4 hotOutlier']

prov_Ag_map['COVID_quadrant_names']=[labels[i] for i in prov_Ag_map['COVID_quadrant']]

prov_Ag_map['COVID_quadrant_names'].value_counts()
Out[66]:
COVID_quadrant_names
0 no_sig         131
1 hotSpot         32
3 coldSpot        21
4 hotOutlier       9
2 coldOutlier      3
Name: count, dtype: int64
In [67]:
from matplotlib import colors
myColMap = colors.ListedColormap([ 'ghostwhite', 'red', 'green', 'black','orange'])

f, ax = plt.subplots(1, figsize=(12,12))


plt.title('Spots and Outliers')

prov_Ag_map.plot(column='COVID_quadrant_names', 
                categorical=True,
                cmap=myColMap,
                linewidth=0.1, 
                edgecolor='white',
                legend=True,
                legend_kwds={'loc': 'center left', 
                             'bbox_to_anchor': (0.7, 0.6)},
                ax=ax)
# Remove axis
ax.set_axis_off()
# Display the map
plt.show()
No description has been provided for this image
In [69]:
import folium

map1=prov_Ag_map[prov_Ag_map.COVID_quadrant_names=='1 hotSpot']
map2=prov_Ag_map[prov_Ag_map.COVID_quadrant_names=='2 coldOutlier']
map3=prov_Ag_map[prov_Ag_map.COVID_quadrant_names=='3 coldSpot']
map4=prov_Ag_map[prov_Ag_map.COVID_quadrant_names=='4 hotOutlier']

m = map1.explore(
    color="red",  
    tooltip=False,  # hide tooltip
    popup=["location"],  # (on-click)
    name="hotSpot"  # name of the layer in the map
)

map2.explore(
    m=m, # notice
    color="green",  
    tooltip=False,  
    popup=["location"],
    name="coldOutlier"
)

map3.explore(
    m=m,
    color="black",  
    tooltip=False,  
    popup=["location"],
    name="coldSpot", 
)

map4.explore(
    m=m,
    color="orange", 
    tooltip=False,  
    popup=["location"],
    name="hotOutlier", 
)

folium.TileLayer("CartoDB positron", show=False).add_to(m)  # use folium to add alternative tiles
folium.LayerControl(collapsed=True).add_to(m)  # use folium to add layer control

m  # show map
Out[69]:
Make this Notebook Trusted to load map: File -> Trust Notebook

Bivariate Lisa

In [70]:
#from esda.moran import Moran_BV, Moran_Local_BV
from esda.moran import Moran_BV

mbi = Moran_BV(prov_Ag_map['year2021'],  prov_Ag_map['year2022'],  w_queen)
mbi.I,mbi.p_sim
Out[70]:
(np.float64(0.14224711545767468), np.float64(0.005))
In [71]:
# The scatterplot with local info
from esda.moran import Moran_Local_BV

# calculate Moran_Local and plot
lisaCOVID_bv = Moran_Local_BV(y=prov_Ag_map['year2021'],
                               x=prov_Ag_map['year2022'],
                               w=w_queen)

fig, ax = moran_scatterplot(lisaCOVID_bv, p=0.05,aspect_equal=True)

ax.set_xlabel('COVID_2022')
ax.set_ylabel('SpatialLag_COVID_2021')
plt.show()
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In [72]:
prov_Ag_map['COVID_quadrant_21_22']=[l if p <0.05 else 0 for l,p in zip(lisaCOVID_bv.q,lisaCOVID_bv.p_sim)  ]

labels = [ '0 no_sig', '1 hotSpot', '2 coldOutlier', '3 coldSpot', '4 hotOutlier']

prov_Ag_map['COVID_quadrant_21_22_names']=[labels[i] for i in prov_Ag_map['COVID_quadrant_21_22']]
In [73]:
# see new columns
prov_Ag_map
Out[73]:
OBJECTID geometry location DEPARTAMENTO PROVINCIA_y year2020 year2021 year2022 year2023 year2024 flag year_2022_qt COVID_quadrant COVID_quadrant_names COVID_quadrant_21_22 COVID_quadrant_21_22_names
0 1.0 POLYGON ((-77.72614 -5.94354, -77.72486 -5.943... AMAZONAS+CHACHAPOYAS AMAZONAS CHACHAPOYAS 1.000000 0.448919 0.414784 0.219178 0.322581 both 1.478159 0 0 no_sig 0 0 no_sig
1 2.0 POLYGON ((-78.61909 -4.51001, -78.61802 -4.510... AMAZONAS+BAGUA AMAZONAS BAGUA 1.000000 0.144654 0.233813 0.240000 0.470588 both 1.027998 0 0 no_sig 0 0 no_sig
2 3.0 POLYGON ((-77.72759 -5.1403, -77.72361 -5.1406... AMAZONAS+BONGARA AMAZONAS BONGARA 1.000000 0.291971 0.466019 0.454545 0.928571 both 1.720078 0 0 no_sig 0 0 no_sig
3 4.0 POLYGON ((-77.81399 -2.99278, -77.81483 -2.995... AMAZONAS+CONDORCANQUI AMAZONAS CONDORCANQUI 1.000000 0.033333 0.025000 0.000000 0.000000 both -0.502255 1 1 hotSpot 0 0 no_sig
4 5.0 POLYGON ((-78.13023 -5.9037, -78.13011 -5.9041... AMAZONAS+LUYA AMAZONAS LUYA 1.000000 0.036585 0.059361 0.111111 0.750000 both -0.140240 0 0 no_sig 0 0 no_sig
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
191 192.0 POLYGON ((-80.28521 -3.41276, -80.28406 -3.412... TUMBES+ZARUMILLA TUMBES ZARUMILLA 0.923077 0.315556 0.492958 0.588235 1.000000 both 1.827310 1 1 hotSpot 0 0 no_sig
192 193.0 POLYGON ((-74.47145 -7.27617, -74.47052 -7.277... UCAYALI+CORONELPORTILLO UCAYALI CORONEL PORTILLO 0.996875 0.186831 0.503120 0.545455 0.515152 both 1.866251 0 0 no_sig 4 4 hotOutlier
193 194.0 POLYGON ((-73.18146 -9.41174, -73.13475 -9.411... UCAYALI+ATALAYA UCAYALI ATALAYA 0.000000 0.000000 0.000000 0.000000 0.000000 both -5.199338 0 0 no_sig 0 0 no_sig
194 195.0 POLYGON ((-75.43663 -8.22999, -75.43651 -8.230... UCAYALI+PADREABAD UCAYALI PADRE ABAD 0.000000 0.078512 0.193694 0.000000 0.000000 both 0.778268 0 0 no_sig 0 0 no_sig
195 196.0 POLYGON ((-70.6138 -9.87339, -70.6214 -9.87808... UCAYALI+PURUS UCAYALI PURUS 0.000000 0.000000 0.000000 1.000000 0.000000 both -5.199338 0 0 no_sig 0 0 no_sig

196 rows × 16 columns

In [74]:
from matplotlib import colors
myColMap = colors.ListedColormap([ 'ghostwhite', 'red', 'green', 'black','orange'])


f, ax = plt.subplots(1, figsize=(12,12))

plt.title('Spots and Outliers')

prov_Ag_map.plot(column='COVID_quadrant_21_22_names', 
                categorical=True,
                cmap=myColMap,
                linewidth=0.1, 
                edgecolor='white',
                legend=True,
                legend_kwds={'loc': 'center left', 
                             'bbox_to_anchor': (0.7, 0.6)},
                ax=ax)
# Remove axis
ax.set_axis_off()
# Display the map
plt.show()
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